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セマンティックセグメンテーション
Semantic Segmentation On Isaid
Semantic Segmentation On Isaid
評価指標
mIoU
評価結果
このベンチマークにおける各モデルのパフォーマンス結果
Columns
モデル名
mIoU
Paper Title
Repository
ViTAE-B + RVSA-UperNet
64.49
Advancing Plain Vision Transformer Towards Remote Sensing Foundation Model
FarSeg@ResNet-50
63.71
Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery
AerialFormer-S
68.4
AerialFormer: Multi-resolution Transformer for Aerial Image Segmentation
SegNeXt-L
70.3
SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation
FarSeg++@Swin-T
66.3
FarSeg++: Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery
RSP-ResNet-50-UperNet
61.6
An Empirical Study of Remote Sensing Pretraining
SegNeXt-S
68.8
SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation
DeepLabV3 with R-50
67.03
Resolution-Aware Design of Atrous Rates for Semantic Segmentation Networks
-
SegNeXt-B
69.9
SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation
SegNeXt-T
68.3
SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation
ViT-B + RVSA-UperNet
63.85
Advancing Plain Vision Transformer Towards Remote Sensing Foundation Model
IMP-ViTAEv2-S-UperNet
65.3
An Empirical Study of Remote Sensing Pretraining
FarSeg++@MiT-B2
67.9
FarSeg++: Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery
FarSeg++@ResNet-50
67.6
FarSeg++: Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery
FactSeg@ResNet-50
64.79
FactSeg: Foreground Activation Driven Small Object Semantic Segmentation in Large-Scale Remote Sensing Imagery
RSP-Swin-T-UperNet
64.1
An Empirical Study of Remote Sensing Pretraining
AerialFormer-T
67.5
AerialFormer: Multi-resolution Transformer for Aerial Image Segmentation
AerialFormer-B
69.3
AerialFormer: Multi-resolution Transformer for Aerial Image Segmentation
RSP-ViTAEv2-S-UperNet
64.3
An Empirical Study of Remote Sensing Pretraining
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